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Deadline: The position is open until filled Research Opportunities in Statistical Learning at the School of Physical and Mathematical Sciences of NTU Singapore There will be multiple PhD positions available
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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multiple priorities and a multi-disciplinary environment. Qualifications: Minimum Bachelor’s Degree in Machine Learning, Data Science, or Statistics, or another field of science or engineering with relevant
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processing; ensuring the provision of necessary instrument inputs for the development of level 1 processing tools/algorithms and the associated ground processor prototype; ensuring the timely availability
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quantum processors using this technological platform design and implement optimization techniques for full-stack improvement of quantum algorithms model major sources of experimental error for control
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. This work involves developing novel techniques, algorithms, and software packages that enable more robust and scalable approaches to cybersecurity using AI-based techniques. In addition to technical
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, molecular properties, and pathological images. Strong knowledge and experience in data science algorithms, methods, and analysis techniques. Experience in programming using Python and R languages and working
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approaches for important questions in neuroscience. We have multiple current and incoming NIH projects to establish cellular cell type architecture maps of mammalian brains using mice as an animal model. Three
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decision with multiple data sources. One example is to develop the semi-supervised methods and dynamic system interfacing algorithms to produce an automated and real-time information exchange across
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within tissues using our in-house developed spatial transcriptomics-based technology (Spatial VDJ). Using established and newly developed algorithms, we map B cell evolution within tissues, including class